Learning Reputation in an Authorship Network
type: Distribution
access URL: https://hal.science/hal-00908762
format: HTML
issued: 2026-05-08T02:54:18.473784
modified: 2026-05-08T02:54:18.457370

Learning Reputation in an Authorship Network
type: Dataset
contact point: Organization
  fn: CCSD
keyword: graph-centrality
keyword: infoeu-reposemanticspreprint
keyword: statmlstatistics-statmachine-learning-statml
keyword: preprints-working-papers-
keyword: expert-finding
keyword: reputation-assessment
keyword: rank-aggregation
landing page: https://hal.science/hal-00908762
description: The problem of searching for experts in a given academic field is hugely important in both industry and academia. We study exactly this issue with respect to a database of authors and their publications. The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform topic modelling in order to find authors who have worked in a query field. We then construct a coauthorship graph and motivate the use of influence maximisation and a variety of graph centrality measures to obtain a ranked list of experts. The ranked lists are further improved using a Markov Chain-based rank aggregation approach. The complete method is readily scalable to large datasets. To demonstrate the efficacy of the approach we report on an extensive set of computational simulations using the Arnetminer dataset. An improvement in mean average precision is demonstrated over the baseline case of simply using the order of authors found by the topic models.
issued: 2026-05-08T02:54:18.465820
language: en
modified: 2026-05-08T02:54:18.465824

test_moissonnage_selune
type: Agent